model-theft
Detects inference endpoints without authentication or throttling, allowing model weight reconstruction. Use when writing inference API endpoints, deploying LLM-serving infrastructure, implementing model access controls, or configuring rate limiting and authentication for model endpoints.
Install
With your agent
Install OMGSkills skill "thejefflarson/soundcheck:.claude/skills/model-theft" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/thejefflarson/soundcheck /tmp/soundcheck && ln -s /tmp/soundcheck/.claude/skills/model-theft ~/.claude/skills/model-theftFrom README
Model Theft (OWASP LLM10:2025) What this checks Prevents unauthorized replication of proprietary models through API abuse. Unauthenticated or unthrottled inference endpoints let attackers systematically query a model to reconstruct its weights or distill a clone — stealing the commercial and IP value of the deployment. Vulnerable patterns Inference endpoint has no authentication — any client can query freely Rate limiting applied per IP only, trivially bypassed with rotating proxies Response includes raw logprobs or full embedding vectors, enabling extraction No monitoring for systematic/grid-search query patterns that signal extraction attempts Fix immediately Flag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties: Every inference endpoint requires authentication — API key, bearer token, or mTLS. Unauthenticated endpoints are free training data for anyone who wants to clone the model.
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